CIET's Ask Us Anything series is a free monthly Q&A session with industry experts.
The How to Analyze Your Energy Data session featured Stephen Dixon, CIET Trainer and RETScreen® Expert.
If you missed it, here is a key topic that was discussed during the session.
A baseline that's perfectly flat out of a CUSUM, a baseline period, means that you have all the variation explained by the model that actually is your baseline model. Statistically speaking, what that means is that your R-squared value is very close to one; very high. So ideally, yes, you want a baseline model with a high R-squared value. But you also have to keep an eye on the other statistics like the T ratio, making sure all the variables you've included deserve to be in the equation.
The other one that is important from the point of view of the precision of the result, or the amount of uncertainty in the result, is the CV RMSE (coefficient of the variation of the root mean square error). You want to make sure that number is below 10%. It depends who you talk to, but I like to see it below 10%. Many people say 15-20%; under that is fine. 10% is better. That's really the uncertainty in your data.
But the big one: if you don't have a flat baseline, you probably have a low R-squared. Many of you know Stephen. He likes to see high R-squared values. I like to see the R-squared values in the 90s. Keep in mind what the term R-squared really means: what amount of the variation, or what percentage of the variation, in the data is described by the model? So if you're not describing 90 to 95% of the data with your model, you're probably missing a variable. The idea of having a non-flat baseline may be that you don't have all the variables in your baseline equation that you need.
Here's another video highlight.
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